Grid enabled virtual screening against malaria

34 pages, 5 figures, 3 tables, to appear in Journal of Grid Computing - PCSV, à paraître dans Journal of Grid ComputingWISDOM is an international initiative to enable a virtual screening pipeline on a grid infrastructure. Its first attempt was to deploy large scale in silico docking on a public grid infrastructure. Protein-ligand docking is about computing the binding energy of a protein target to a library of potential drugs using a scoring algorithm. Previous deployments were either limited to one cluster, to grids of clusters in the tightly protected environment of a pharmaceutical laboratory or to pervasive grids. The first large scale docking experiment ran on the EGEE grid production service from 11 July 2005 to 19 August 2005 against targets relevant to research on malaria and saw over 41 million compounds docked for the equivalent of 80 years of CPU time. Up to 1,700 computers were simultaneously used in 15 countries around the world. Issues related to the deployment and the monitoring of the in silico docking experiment as well as experience with grid operation and services are reported in the paper. The main problem encountered for such a large scale deployment was the grid infrastructure stability. Although the overall success rate was above 80%, a lot of monitoring and supervision was still required at the application level to resubmit the jobs that failed. But the experiment demonstrated how grid infrastructures have a tremendous capacity to mobilize very large CPU resources for well targeted goals during a significant period of time. This success leads to a second computing challenge targeting Avian Flu neuraminidase N1

High Throughput Virtual Screening with Data Level Parallelism in Multi-core Processors

Improving the throughput of molecular docking, a computationally intensive phase of the virtual screening process, is a highly sought area of research since it has a significant weight in the drug designing process. With such improvements, the world might find cures for incurable diseases like HIV disease and Cancer sooner. Our approach presented in this paper is to utilize a multi-core environment to introduce Data Level Parallelism (DLP) to the Autodock Vina software, which is a widely used for molecular docking software. Autodock Vina already exploits Instruction Level Parallelism (ILP) in multi-core environments and therefore optimized for such environments. However, with the results we have obtained, it can be clearly seen that our approach has enhanced the throughput of the already optimized software by more than six times. This will dramatically reduce the time consumed for the lead identification phase in drug designing along with the shift in the processor technology from multi-core to many-core of the current era. Therefore, we believe that the contribution of this project will effectively make it possible to expand the number of small molecules docked against a drug target and improving the chances to design drugs for incurable diseases.Comment: Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference o

gcodeml: A Grid-enabled Tool for Detecting Positive Selection in Biological Evolution

One of the important questions in biological evolution is to know if certain changes along protein coding genes have contributed to the adaptation of species. This problem is known to be biologically complex and computationally very expensive. It, therefore, requires efficient Grid or cluster solutions to overcome the computational challenge. We have developed a Grid-enabled tool (gcodeml) that relies on the PAML (codeml) package to help analyse large phylogenetic datasets on both Grids and computational clusters. Although we report on results for gcodeml, our approach is applicable and customisable to related problems in biology or other scientific domains.Comment: 10 pages, 4 figures. To appear in the HealthGrid 2012 con

Identification de gènes diagnostic chez les Rhizobium leguminosarum à l'aide de la grille informatique sous l'environnement WISDOM

Identification de gènes diagnostic chez les Rhizobium leguminosarum à l'aide de la grille informatique sous l'environnement WISDO

Innovative in silico approaches to address avian flu using grid technology

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper presents how we are currently exploring how to integrate the existing data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target

DOVIS: an implementation for high-throughput virtual screening using AutoDock

<p>Abstract</p> <p>Background</p> <p>Molecular-docking-based virtual screening is an important tool in drug discovery that is used to significantly reduce the number of possible chemical compounds to be investigated. In addition to the selection of a sound docking strategy with appropriate scoring functions, another technical challenge is to <it>in silico </it>screen millions of compounds in a reasonable time. To meet this challenge, it is necessary to use high performance computing (HPC) platforms and techniques. However, the development of an integrated HPC system that makes efficient use of its elements is not trivial.</p> <p>Results</p> <p>We have developed an application termed DOVIS that uses AutoDock (version 3) as the docking engine and runs in parallel on a Linux cluster. DOVIS can efficiently dock large numbers (millions) of small molecules (ligands) to a receptor, screening 500 to 1,000 compounds per processor per day. Furthermore, in DOVIS, the docking session is fully integrated and automated in that the inputs are specified via a graphical user interface, the calculations are fully integrated with a Linux cluster queuing system for parallel processing, and the results can be visualized and queried.</p> <p>Conclusion</p> <p>DOVIS removes most of the complexities and organizational problems associated with large-scale high-throughput virtual screening, and provides a convenient and efficient solution for AutoDock users to use this software in a Linux cluster platform.</p

Grid enabled high throughput virtual screening against four different targets implicated in malaria

PCSVInternational audienceAfter having deployed a first data challenge on malaria and a second one on avian flu, respectively in summer 2005 and spring 2006, we are demonstrating here again how efficiently the computational grids can be used to produce massive docking data at a high-throughput. During more than 2 months and a half, we have achieved at least 140 million dockings, representing an average throughput of almost 80,000 dockings per hour. This was made possible by the availability of thousands of CPUs through different infrastructures worldwide. Through the acquired experience, the WISDOM production environment is evolving to enable an easy and fault-tolerant deployment of biological tools; in this case it is the FlexX commercial docking software which is used to dock the whole ZINC database against 4 different targets

Large scale deployment of molecular docking application on computational grid infrastructures for combating malaria

PCSVInternational audienceComputational grids are solutions for several biological applications like virtual screening or molecular dynamics where large amounts of computing power and storage are required. The WISDOM project successfully deployed virtual screening at large scale on EGEE grid infrastructures in the summer 2005 and achieved 46 million dockings in 45 days, which is equivalent to 80 CPU years. WISDOM is one good example of a successful deployment of an embarrassingly parallel application. In this paper, we describe the improvements in our deployment. We screened ZINC database against four targets implicated in malaria. During more than 2 months and a half, we have achieved 140 million dockings, representing an average throughput of almost 80,000 dockings per hour. This was made possible by the availability of thousands of CPUs through different infrastructures worldwide. Through the acquired experience, the WISDOM production environment is evolving to enable an easy and fault-tolerant deployment of biological tool

Discovery of potent, novel, non-toxic anti-malarial compounds via quantum modelling, virtual screening and in vitro experimental validation

<p>Abstract</p> <p>Background</p> <p>Developing resistance towards existing anti-malarial therapies emphasize the urgent need for new therapeutic options. Additionally, many malaria drugs in use today have high toxicity and low therapeutic indices. Gradient Biomodeling, LLC has developed a quantum-model search technology that uses quantum similarity and does not depend explicitly on chemical structure, as molecules are rigorously described in fundamental quantum attributes related to individual pharmacological properties. Therapeutic activity, as well as toxicity and other essential properties can be analysed and optimized simultaneously, independently of one another. Such methodology is suitable for a search of novel, non-toxic, active anti-malarial compounds.</p> <p>Methods</p> <p>A set of innovative algorithms is used for the fast calculation and interpretation of electron-density attributes of molecular structures at the quantum level for rapid discovery of prospective pharmaceuticals. Potency and efficacy, as well as additional physicochemical, metabolic, pharmacokinetic, safety, permeability and other properties were characterized by the procedure. Once quantum models are developed and experimentally validated, the methodology provides a straightforward implementation for lead discovery, compound optimizzation and <it>de novo </it>molecular design.</p> <p>Results</p> <p>Starting with a diverse training set of 26 well-known anti-malarial agents combined with 1730 moderately active and inactive molecules, novel compounds that have strong anti-malarial activity, low cytotoxicity and structural dissimilarity from the training set were discovered and experimentally validated. Twelve compounds were identified <it>in silico </it>and tested <it>in vitro</it>; eight of them showed anti-malarial activity (IC50 ≤ 10 μM), with six being very effective (IC50 ≤ 1 μM), and four exhibiting low nanomolar potency. The most active compounds were also tested for mammalian cytotoxicity and found to be non-toxic, with a therapeutic index of more than 6,900 for the most active compound.</p> <p>Conclusions</p> <p>Gradient's metric modelling approach and electron-density molecular representations can be powerful tools in the discovery and design of novel anti-malarial compounds. Since the quantum models are agnostic of the particular biological target, the technology can account for different mechanisms of action and be used for <it>de novo </it>design of small molecules with activity against not only the asexual phase of the malaria parasite, but also against the liver stage of the parasite development, which may lead to true causal prophylaxis.</p